TipMate: An Explainable Fair Tip Recommender System Based on LLM

Tipping, which has evolved beyond its original role as a gesture of appreciation, has become a complex social issue that often causes friction between customers and service workers. Existing technological approaches, instead of clarifying the ambiguity in evaluation criteria, merely provide predefined tipping options that fail to improve customer satisfaction. Such practices lead to 'tip fatigue' and foster a sense of coercion regardless of service quality, thereby intensifying social tension. This issue arises from the lack of objective and transparent standards for evaluating service value. To address these limitations, this paper proposes TipMate, an explainable fair tip recommender system based on Large Language Models (LLMs) that holistically evaluates service quality and transparently explains its reasoning. TipMate conducts evaluations grounded in the established service quality theory by systematically integrating 1) visual evidence from the service environment, 2) subjective textual feedback from customers, and 3) contextual data from publicly available reviews. To validate the system's reasoning capabilities, we constructed OpenR-Tip, a benchmark dataset derived from real-world narratives with synthesized contexts. Experimental results using four state-of-the-art LLMs show that TipMate closely approximates human tipping judgments with a Mean Absolute Error (MAE) of 4.26%, achieving a high average satisfaction score of 4.43/5 in a user study.